Cargando…
Distance measures for tumor evolutionary trees
MOTIVATION: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference method...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141873/ https://www.ncbi.nlm.nih.gov/pubmed/31750900 http://dx.doi.org/10.1093/bioinformatics/btz869 |
_version_ | 1783519274768269312 |
---|---|
author | DiNardo, Zach Tomlinson, Kiran Ritz, Anna Oesper, Layla |
author_facet | DiNardo, Zach Tomlinson, Kiran Ritz, Anna Oesper, Layla |
author_sort | DiNardo, Zach |
collection | PubMed |
description | MOTIVATION: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS: Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION: Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7141873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71418732020-04-13 Distance measures for tumor evolutionary trees DiNardo, Zach Tomlinson, Kiran Ritz, Anna Oesper, Layla Bioinformatics Original Papers MOTIVATION: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS: Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION: Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-04-01 2019-11-21 /pmc/articles/PMC7141873/ /pubmed/31750900 http://dx.doi.org/10.1093/bioinformatics/btz869 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers DiNardo, Zach Tomlinson, Kiran Ritz, Anna Oesper, Layla Distance measures for tumor evolutionary trees |
title | Distance measures for tumor evolutionary trees |
title_full | Distance measures for tumor evolutionary trees |
title_fullStr | Distance measures for tumor evolutionary trees |
title_full_unstemmed | Distance measures for tumor evolutionary trees |
title_short | Distance measures for tumor evolutionary trees |
title_sort | distance measures for tumor evolutionary trees |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141873/ https://www.ncbi.nlm.nih.gov/pubmed/31750900 http://dx.doi.org/10.1093/bioinformatics/btz869 |
work_keys_str_mv | AT dinardozach distancemeasuresfortumorevolutionarytrees AT tomlinsonkiran distancemeasuresfortumorevolutionarytrees AT ritzanna distancemeasuresfortumorevolutionarytrees AT oesperlayla distancemeasuresfortumorevolutionarytrees |